AutoML handles categorical data by automating the preprocessing and encoding steps required to convert non-numerical features into formats suitable for machine learning models. Categorical data, such as text labels (e.g., “red,” “blue”) or discrete categories (e.g., “high,” “medium,” “low”), must be transformed into numerical representations because most algorithms cannot process raw text. AutoML tools typically apply techniques like one-hot encoding, label encoding, or target encoding based on the data’s characteristics. For example, one-hot encoding converts each category into a binary column (0 or 1), which works well for features with a limited number of unique values. Label encoding assigns an integer to each category (e.g., "high"=1, "medium"=2), which is useful for ordinal data. AutoML systems often analyze the data type, cardinality (number of unique categories), and relationship to the target variable to select the most appropriate method automatically.
When dealing with high-cardinality categorical features (e.g., a “city” column with hundreds of unique values), AutoML might use strategies like frequency encoding (replacing categories with their occurrence counts) or embeddings (learned low-dimensional representations). Some tools also apply dimensionality reduction or clustering to group rare categories. For instance, if a “product ID” column has thousands of unique values, AutoML could cluster IDs based on their interaction with other features (e.g., purchase frequency) to reduce noise. Additionally, frameworks like Google’s AutoML Tables or H2O Driverless AI automatically detect categorical columns and apply optimizations, such as using target encoding (replacing categories with the mean of the target variable) for high-cardinality features to avoid creating sparse one-hot encoded matrices.
AutoML also handles missing or inconsistent categorical values by imputing them with placeholders like “unknown” or using the most frequent category. For example, if a “color” column has missing entries, the system might fill them with “missing” or infer values from other features. Rare categories (e.g., a country name appearing only once) might be grouped into an “other” bucket to prevent overfitting. Advanced systems may even perform feature interactions, such as combining “zip code” and “income level” to create a new categorical feature. By testing different encoding strategies during cross-validation, AutoML ensures the chosen approach balances model performance and computational efficiency, allowing developers to focus on higher-level tasks without manual tuning.